{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "603cbc2d-e64b-42ed-8f70-4cb00b0451b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "#######################################################\n",
    "# 第1步：载入数据\n",
    "#######################################################\n",
    "import torch \n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import torch.optim as optim\n",
    "import os\n",
    "import os.path\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),                                  #将PILImage转换为张量\n",
    "        transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))      #将[0,1] 归一化到[-1,1]\n",
    "    ]\n",
    ")\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(\n",
    "    root='/data/project/python/torch/data/cifar10',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "trainloader = torch.utils.data.DataLoader(\n",
    "    trainset,\n",
    "    batch_size=4,                                              #每个batch载入图片的数量，默认为1\n",
    "    shuffle=True,\n",
    "    num_workers=2\n",
    ")\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(\n",
    "    root='/data/project/python/torch/data/cifar10',\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "testloader = torch.utils.data.DataLoader(\n",
    "    trainset,\n",
    "    batch_size=4,\n",
    "    shuffle=False,\n",
    "    num_workers=2\n",
    ")\n",
    "\n",
    "cifar10_classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'trunk')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0e8bc10-fcbd-4857-bf23-740be1316c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################\n",
    "# 查看训练数据\n",
    "# 备注：该部分代码不可放入主函数\n",
    "#######################################################\n",
    "# import numpy as np\n",
    "# dataiter = iter(trainloader)\n",
    "# images, lables = dataiter.next()\n",
    "# images.shape\n",
    "# torchvision.utils.save_image(image[1], '/data/project/python/torch/result/vgg16/test.jpg')\n",
    "# cifar10_classes[labels[j]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34d840d1-e0c0-43ce-ac24-2351a65843f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "#######################################################\n",
    "# 第2步：构建神经网络\n",
    "#######################################################\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "cfg = {\n",
    "    'VGG16': [\n",
    "        64, 64, 'M', \n",
    "        128, 128, 'M',\n",
    "        256, 256, 256, 'M',\n",
    "        512, 512, 512, 'M',\n",
    "        512, 512, 512, 'M'\n",
    "    ]\n",
    "}\n",
    "\n",
    "class VGG(nn.Module):\n",
    "    def __init__(self, net_name):\n",
    "        super(VGG, self).__init__()\n",
    "\n",
    "        # 构建网络的卷积层和池化层\n",
    "        self.features = self._make_layers(cfg[net_name])\n",
    "        \n",
    "        # 构建卷积层之后的全连接层及分类器\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(512, 512),        # fc1\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(512, 512),        # fc2\n",
    "            nn.ReLU(True),\n",
    "            nn.Linear(512,10)           # fc3\n",
    "        )\n",
    "\n",
    "        # 初始化权重\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
    "                m.weight.data.normal_(0, math.sqrt(2. / n))\n",
    "                m.bias.data.zero_()\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "    def _make_layers(self, cfg):\n",
    "        layers = []\n",
    "        in_channels = 3\n",
    "        for v in cfg:\n",
    "            if v == 'M':\n",
    "                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n",
    "            else:\n",
    "                layers += [\n",
    "                    nn.Conv2d(in_channels, v, kernel_size=3, padding=1),\n",
    "                    nn.BatchNorm2d(v),\n",
    "                    nn.ReLU(inplace=True)\n",
    "                ]\n",
    "                in_channels = v\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "net = VGG('VGG16')\n",
    "# print(net)\n",
    "\n",
    "#######################################################\n",
    "# 第3步：定义损失函数和优化方法\n",
    "#######################################################\n",
    "\n",
    "\n",
    "learn_rate = 0.0001\n",
    "momentum = 0.9\n",
    "\n",
    "#x=torch.randn(2,3,32,32)\n",
    "#y=net(x)\n",
    "#print(y.size())\n",
    "\n",
    "# 定义损失函数：交叉熵\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# 定义优化方法：随机梯度下降法\n",
    "optimizer = optim.SGD(net.parameters(), lr=learn_rate, momentum=momentum)\n",
    "\n",
    "#######################################################\n",
    "# 第4步：训练网络\n",
    "#######################################################\n",
    "for epoch in range(5):\n",
    "    train_loss = 0.0\n",
    "    for batch_idx, data in enumerate(trainloader, 0):\n",
    "        # 初始化\n",
    "        inputs, labels = data                    #获取数据\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # 优化进程\n",
    "        outputs = net(inputs)\n",
    "\n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        # 查看网络训练状况\n",
    "        train_loss += loss.item()\n",
    "        # if batch_idx % 2000 == 1999:\n",
    "        #     print('[%d %5d], loss: %.3f' % (epoch+1, batch_idx+1, train_loss / 2000))\n",
    "        #     train_loss = 0.0\n",
    "\n",
    "\n",
    "        print('[%d %5d], loss: %.3f' % (epoch+1, batch_idx+1, train_loss / 2000))\n",
    "        train_loss = 0.0\n",
    "\n",
    "        print('Saving epoch %d model ...' % (epoch+1))\n",
    "        state = {\n",
    "            'net' : net.state_dict(),\n",
    "            'epoch' : epoch + 1\n",
    "        }\n",
    "        if not os.path.isdir('checkpoint'):\n",
    "            os.mkdir('checkpoint')\n",
    "        torch.save(state, '/data/project/python/torch/result/vgg16/checkpoint/cifar10_epoch_%d.ckpt' % (epoch+1))\n",
    "\n",
    "print('Finished Traing')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "944b7065-1a5d-47bc-a51e-99aeffa70f74",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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